ML Session n°3

Transcript

1. ML: regression March 2017

2. Notations : Scalar (number) : Vector : Matrix : Transpose

   of matrix X : Mean of vectors x : Estimate of vector value x

3. Simple linear regression

4. Solving simple linear regression Goal : find the best and

   for Best ? Minimize the square residuals (least squares)

5. Solving simple linear regression: finding the best estimates for a

   and b

6. Demo 1

7. Multivariate linear regression

8. Demo 2

9. Polynomial regression

10. Trick : still a linear regression ! Just create additional

    columns, derived from pre-existing ones Then it comes back to a linear regression

11. Demo 3

12. Gradient descent

13. Gradient descent

14. Gradient descent

15. Normal equation vs gradient descent Normal equation Gradient descent No

    additional parameters Need to choose a learning step No loop Needs to iterate : for inverse Slow if is large Works well when is large In practice : n < 10.000 ⇔ normal equation

16. Logistic regression Used for binary classification Decision boundary : 0.5

    - y < 0.5 : class A - y >= 0.5 : class B

17. Logistic regression : cost function

18. Logistic regression : cost function

19. Demo 4: logistic regression + gradient descent

20. Questions? March 2017